Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data
The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each s...
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001058 |
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| author | Yuze Hou Patrick Schneider Linda Ney Nada Zamel |
| author_facet | Yuze Hou Patrick Schneider Linda Ney Nada Zamel |
| author_sort | Yuze Hou |
| collection | DOAJ |
| description | The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research. |
| format | Article |
| id | doaj-art-3c6f9e3085384762ae565f8a48447d59 |
| institution | OA Journals |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-3c6f9e3085384762ae565f8a48447d592025-08-20T02:36:58ZengElsevierEnergy and AI2666-54682024-12-011810043910.1016/j.egyai.2024.100439Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental dataYuze Hou0Patrick Schneider1Linda Ney2Nada Zamel3Corresponding author.; Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, GermanyFraunhofer Institute for Solar Energy Systems ISE, Freiburg, GermanyFraunhofer Institute for Solar Energy Systems ISE, Freiburg, GermanyFraunhofer Institute for Solar Energy Systems ISE, Freiburg, GermanyThe catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research.http://www.sciencedirect.com/science/article/pii/S2666546824001058PEM fuel cellMachine LearningCatalyst layer productionCharacterization |
| spellingShingle | Yuze Hou Patrick Schneider Linda Ney Nada Zamel Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data Energy and AI PEM fuel cell Machine Learning Catalyst layer production Characterization |
| title | Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data |
| title_full | Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data |
| title_fullStr | Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data |
| title_full_unstemmed | Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data |
| title_short | Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data |
| title_sort | optimizing catalyst layer composition of pem fuel cell via machine learning insights from in house experimental data |
| topic | PEM fuel cell Machine Learning Catalyst layer production Characterization |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001058 |
| work_keys_str_mv | AT yuzehou optimizingcatalystlayercompositionofpemfuelcellviamachinelearninginsightsfrominhouseexperimentaldata AT patrickschneider optimizingcatalystlayercompositionofpemfuelcellviamachinelearninginsightsfrominhouseexperimentaldata AT lindaney optimizingcatalystlayercompositionofpemfuelcellviamachinelearninginsightsfrominhouseexperimentaldata AT nadazamel optimizingcatalystlayercompositionofpemfuelcellviamachinelearninginsightsfrominhouseexperimentaldata |